Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: A classification framework for predicting components' remaining useful life based on discrete-event diagnostic data
Authors: Fink, Olga
Zio, Enrico
Weidmann, Ulrich
DOI: 10.1109/TR.2015.2440531
Published in: IEEE Transactions on Reliability
Volume(Issue): 64
Issue: 3
Page(s): 1049
Pages to: 1056
Issue Date: 2015
Publisher / Ed. Institution: IEEE
ISSN: 0018-9529
Language: English
Subject (DDC): 006: Special computer methods
Abstract: In this paper, we propose to define the problem of predicting the remaining useful life of a component as a binary classification task. This approach is particularly useful for problems in which the evolution of the system condition is described by a combination of a large number of discrete-event diagnostic data, and for which alternative approaches are either not applicable, or are only applicable with significant limitations or with a large computational burden. The proposed approach is demonstrated with a case study of real discrete-event data for predicting the occurrence of railway operation disruptions. For the classification task, Extreme Learning Machine (ELM) has been chosen because of its good generalization ability, computational efficiency, and low requirements on parameter tuning.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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